Chapter 1, Setting Up Spark For Deep Learning, covers everything you need in order to get started developing on Spark within a virtual Ubuntu Desktop environment.
Chapter 2, Creating a Neural Network with Spark, explains the process of developing a neural network from scratch without using any deep learning libraries, such as TensorFlow or Keras.
Chapter 3, Pain Points of Convolutional Neural Networks, walks through some of the pain points associated with working on a convolutional neural network for image recognition, and how they can be overcome.
Chapter 4, Pain Points of Recurrent Neural Networks, covers an introduction to feedforward neural networks and recurrent neural network. We describe some of the pain points that arise with recurrent neural networks and also how to tackle them with the use of LSTMs.
Chapter 5, Predicting Fire Department Calls with Spark ML, walks through developing a classification model for predicting fire department calls from the city of San Francisco using Spark machine learning.
Chapter 6, Using LSTMs in Generative Networks, gives a hands-on approach to using novels or large text corpora as input data to define and train an LSTM model, while also using the trained model to generate its own output sequences.
Chapter 7, Natural Language Processing with TF-IDF, walks through the steps to classify chatbot conversation data for escalation.
Chapter 8, Real Estate Value Prediction Using XGBoost, focuses on using the Kings County House Sales dataset to train a simple linear model and uses it to predict house prices before diving into a slightly more complicated model to do the same and improve prediction accuracy.
Chapter 9, Predicting Apple Stock Market Cost with LSTM, focuses on creating a deep learning model using LSTM on Keras to predict the stock market price of the AAPL stock.
Chapter 10, Face Recognition Using Deep Convolutional Networks, utilizes the MIT-CBCL dataset of facial images of 10 different subjects to train and test a deep convolutional neural network model.
Chapter 11, Creating and Visualizing Word Vectors Using Word2Vec, focuses on the importance of vectors in machine learning, and also walks users through how to utilize Google's Word2Vec model to train a different model and visualize word vectors generated from novels.
Chapter 12, Creating a Movie Recommendation Engine with Keras,  focuses on building a movie recommendation engine for users using the deep learning library Keras.
Chapter 13, Image Classification with TensorFlow on Spark, focuses on leveraging transfer learning to recognize the top two football players in the world: Cristiano Ronaldo and Lionel Messi.
